我问了一个关于在图表中汇总数量的问题earlier。提供的两个答案运作良好,但现在我试图将Cypher查询扩展到可变深度的图表。
总结一下,我们从一堆叶子商店开始,这些叶子商店都与特定供应商相关联,这是Store
节点上的一个属性。然后将库存移至其他商店,每个供应商的比例对应于他们对原始商店的贡献。
因此,对于节点B02
,S2
贡献了750/1250 = 60%
,S3
贡献了40%
。然后,我们将B02
60%
属于S2
和40%
的{{1}}移至600 S3
,依此类推。
我们想知道D01
中最终700个单位的百分比属于每个供应商。供应商名称相同的供应商。因此,对于上图,我们期望:
S1,38.09
S2,27.61
S3,34.28
我已使用此Cypher脚本准备了一个图表:
CREATE (A01:Store {Name: 'A01', Supplier: 'S1'})
CREATE (A02:Store {Name: 'A02', Supplier: 'S1'})
CREATE (A03:Store {Name: 'A03', Supplier: 'S2'})
CREATE (A04:Store {Name: 'A04', Supplier: 'S3'})
CREATE (A05:Store {Name: 'A05', Supplier: 'S1'})
CREATE (A06:Store {Name: 'A06', Supplier: 'S1'})
CREATE (A07:Store {Name: 'A07', Supplier: 'S2'})
CREATE (A08:Store {Name: 'A08', Supplier: 'S3'})
CREATE (B01:Store {Name: 'B01'})
CREATE (B02:Store {Name: 'B02'})
CREATE (B03:Store {Name: 'B03'})
CREATE (B04:Store {Name: 'B04'})
CREATE (C01:Store {Name: 'C01'})
CREATE (C02:Store {Name: 'C02'})
CREATE (D01:Store {Name: 'D01'})
CREATE (A01)-[:MOVE_TO {Quantity: 750}]->(B01)
CREATE (A02)-[:MOVE_TO {Quantity: 500}]->(B01)
CREATE (A03)-[:MOVE_TO {Quantity: 750}]->(B02)
CREATE (A04)-[:MOVE_TO {Quantity: 500}]->(B02)
CREATE (A05)-[:MOVE_TO {Quantity: 100}]->(B03)
CREATE (A06)-[:MOVE_TO {Quantity: 200}]->(B03)
CREATE (A07)-[:MOVE_TO {Quantity: 50}]->(B04)
CREATE (A08)-[:MOVE_TO {Quantity: 450}]->(B04)
CREATE (B01)-[:MOVE_TO {Quantity: 400}]->(C01)
CREATE (B02)-[:MOVE_TO {Quantity: 600}]->(C01)
CREATE (B03)-[:MOVE_TO {Quantity: 100}]->(C02)
CREATE (B04)-[:MOVE_TO {Quantity: 200}]->(C02)
CREATE (C01)-[:MOVE_TO {Quantity: 500}]->(D01)
CREATE (C02)-[:MOVE_TO {Quantity: 200}]->(D01)
目前的查询是:
MATCH (s:Store { Name:'D01' })
MATCH (s)<-[t:MOVE_TO]-()<-[r:MOVE_TO]-(supp)
WITH t.Quantity as total, collect(r) as movements
WITH total, movements, reduce(totalSupplier = 0, r IN movements | totalSupplier + r.Quantity) as supCount
UNWIND movements as movement
RETURN startNode(movement).Supplier as Supplier, round(100.0*movement.Quantity/supCount) as pct
我正在尝试使用递归关系,就像这样:
MATCH (s)<-[t:MOVE_TO]-()<-[r:MOVE_TO*]-(supp)
然而,它提供了到终端节点的多条路径,我需要在每个节点聚合库存。
答案 0 :(得分:3)
正如我之前所说,我喜欢这个问题。我知道你已经接受了答案,但是我决定发布我的最终答案,因为它也会在没有客户努力的情况下返回百分位数(这意味着您还可以在节点上执行SET以在需要时更新数据库中的值)并且当然,如果出于任何其他原因,我可以回来:) 这是console example
的链接它会返回一个包含商店名称的行,从所有供应商处移除的总和以及每个供应商的百分位数
MATCH p =s<-[:MOVE_TO*]-sup
WHERE HAS (sup.Supplier) AND NOT HAS (s.Supplier)
WITH s,sup,reduce(totalSupplier = 0, r IN relationships(p)| totalSupplier + r.Quantity) AS TotalAmountMoved
WITH sum(TotalAmountMoved) AS sumMoved, collect(DISTINCT ([sup.Supplier, TotalAmountMoved])) AS MyDataPart1,s
WITH reduce(b=[], c IN MyDataPart1| b +[{ Supplier: c[0], Quantity: c[1], Percentile: ((c[1]*1.00))/(sumMoved*1.00)*100.00 }]) AS MyData, s, sumMoved
RETURN s.Name, sumMoved, MyData
答案 1 :(得分:2)
我无法通过纯密码的解决方案来思考,因为我认为你不能在密码中做这样的递归。您可以使用cypher以简单的方式返回树中的所有数据,以便您可以使用自己喜欢的编程语言计算它。像这样:
MATCH path=(source:Store)-[move:MOVE_TO*]->(target:Store {Name: 'D01'})
WHERE source.Supplier IS NOT NULL
RETURN
source.Supplier,
reduce(a=[], move IN relationships(path)| a + [{id: ID(move), Quantity: move.Quantity}])
这将返回每条路径上每个关系的ID和数量。然后你可以处理该客户端(可能首先将其转换为嵌套数据结构?)
答案 2 :(得分:2)
此查询为符合问题中描述的模型的任意图形生成正确的结果。 (当Store
x将商品移至Store
y时,系统会假定所移动商品的Supplier
百分比与Store
x相同。)
但是,此解决方案不仅包含单个Cypher查询(因为这可能无法实现)。相反,它涉及多个查询,其中一个必须迭代,直到计算级联通过Store
个节点的整个图形。该迭代查询将清楚地告诉您何时停止迭代。需要其他Cypher查询:为迭代准备图表,报告&#34; end&#34;的供应商百分比。节点,并清理图形(以便它恢复到下面步骤1之前的方式)。
这些查询可能会进一步优化。
以下是必需的步骤:
为迭代查询准备图形(为所有起始pcts
节点初始化临时Store
数组)。这包括创建单个Suppliers
节点,该节点具有包含所有供应商名称的数组。这用于建立临时pcts
数组元素的顺序,并将这些元素映射回正确的供应商名称。
MATCH (store:Store)
WHERE HAS (store.Supplier)
WITH COLLECT(store) AS stores, COLLECT(DISTINCT store.Supplier) AS csup
CREATE (sups:Suppliers { names: csup })
WITH stores, sups
UNWIND stores AS store
SET store.pcts =
EXTRACT(i IN RANGE(0,LENGTH(sups.names)-1,1) |
CASE WHEN store.Supplier = sups.names[i] THEN 1.0 ELSE 0.0 END)
RETURN store.Name, store.Supplier, store.pcts;
以下是问题数据的结果:
+---------------------------------------------+
| store.Name | store.Supplier | store.pcts |
+---------------------------------------------+
| "A01" | "S1" | [1.0,0.0,0.0] |
| "A02" | "S1" | [1.0,0.0,0.0] |
| "A03" | "S2" | [0.0,1.0,0.0] |
| "A04" | "S3" | [0.0,0.0,1.0] |
| "A05" | "S1" | [1.0,0.0,0.0] |
| "A06" | "S1" | [1.0,0.0,0.0] |
| "A07" | "S2" | [0.0,1.0,0.0] |
| "A08" | "S3" | [0.0,0.0,1.0] |
+---------------------------------------------+
8 rows
83 ms
Nodes created: 1
Properties set: 9
迭代查询(重复运行,直到返回0行)
MATCH p=(s1:Store)-[m:MOVE_TO]->(s2:Store)
WHERE HAS(s1.pcts) AND NOT HAS(s2.pcts)
SET s2.pcts = EXTRACT(i IN RANGE(1,LENGTH(s1.pcts),1) | 0)
WITH s2, COLLECT(p) AS ps
WITH s2, ps, REDUCE(s=0, p IN ps | s + HEAD(RELATIONSHIPS(p)).Quantity) AS total
FOREACH(p IN ps |
SET HEAD(RELATIONSHIPS(p)).pcts = EXTRACT(parentPct IN HEAD(NODES(p)).pcts | parentPct * HEAD(RELATIONSHIPS(p)).Quantity / total)
)
FOREACH(p IN ps |
SET s2.pcts = EXTRACT(i IN RANGE(0,LENGTH(s2.pcts)-1,1) | s2.pcts[i] + HEAD(RELATIONSHIPS(p)).pcts[i])
)
RETURN s2.Name, s2.pcts, total, EXTRACT(p IN ps | HEAD(RELATIONSHIPS(p)).pcts) AS rel_pcts;
迭代1结果:
+-----------------------------------------------------------------------------------------------+
| s2.Name | s2.pcts | total | rel_pcts |
+-----------------------------------------------------------------------------------------------+
| "B04" | [0.0,0.1,0.9] | 500 | [[0.0,0.1,0.0],[0.0,0.0,0.9]] |
| "B01" | [1.0,0.0,0.0] | 1250 | [[0.6,0.0,0.0],[0.4,0.0,0.0]] |
| "B03" | [1.0,0.0,0.0] | 300 | [[0.3333333333333333,0.0,0.0],[0.6666666666666666,0.0,0.0]] |
| "B02" | [0.0,0.6,0.4] | 1250 | [[0.0,0.6,0.0],[0.0,0.0,0.4]] |
+-----------------------------------------------------------------------------------------------+
4 rows
288 ms
Properties set: 24
迭代2结果:
+-------------------------------------------------------------------------------------------------------------------------------+
| s2.Name | s2.pcts | total | rel_pcts |
+-------------------------------------------------------------------------------------------------------------------------------+
| "C02" | [0.3333333333333333,0.06666666666666667,0.6] | 300 | [[0.3333333333333333,0.0,0.0],[0.0,0.06666666666666667,0.6]] |
| "C01" | [0.4,0.36,0.24] | 1000 | [[0.4,0.0,0.0],[0.0,0.36,0.24]] |
+-------------------------------------------------------------------------------------------------------------------------------+
2 rows
193 ms
Properties set: 12
迭代3结果:
+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| s2.Name | s2.pcts | total | rel_pcts |
+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| "D01" | [0.38095238095238093,0.27619047619047615,0.34285714285714286] | 700 | [[0.2857142857142857,0.2571428571428571,0.17142857142857143],[0.09523809523809522,0.01904761904761905,0.17142857142857143]] |
+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
1 row
40 ms
Properties set: 6
迭代4结果:
+--------------------------------------+
| s2.Name | s2.pcts | total | rel_pcts |
+--------------------------------------+
+--------------------------------------+
0 rows
69 ms
列出结束Supplier
节点的非零Store
百分比。
MATCH (store:Store), (sups:Suppliers)
WHERE NOT (store:Store)-[:MOVE_TO]->(:Store) AND HAS(store.pcts)
RETURN store.Name, [i IN RANGE(0,LENGTH(sups.names)-1,1) WHERE store.pcts[i] > 0 | {supplier: sups.names[i], pct: store.pcts[i] * 100}] AS pcts;
结果:
+----------------------------------------------------------------------------------------------------------------------------------+
| store.Name | pcts |
+----------------------------------------------------------------------------------------------------------------------------------+
| "D01" | [{supplier=S1, pct=38.095238095238095},{supplier=S2, pct=27.619047619047617},{supplier=S3, pct=34.285714285714285}] |
+----------------------------------------------------------------------------------------------------------------------------------+
1 row
293 ms
清理(删除所有临时pcts
道具和Suppliers
节点)。
MATCH (s:Store), (sups:Suppliers)
OPTIONAL MATCH (s)-[m:MOVE_TO]-()
REMOVE m.pcts, s.pcts
DELETE sups;
结果:
0 rows
203 ms
+-------------------+
| No data returned. |
+-------------------+
Properties set: 29
Nodes deleted: 1